You're reading from The Machine Learning Solutions Architect Handbook Create machine learning platforms to run solutions in an enterprise setting

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781801072168

Length 442 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

David Ping

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Table of Contents (17) Chapters

Preface

1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture

2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture FREE CHAPTER

3. Chapter 2: Business Use Cases for Machine Learning

4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning

5. Chapter 3: Machine Learning Algorithms

6. Chapter 4: Data Management for Machine Learning

7. Chapter 5: Open Source Machine Learning Libraries

8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management

9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

10. Chapter 7: Open Source Machine Learning Platforms

11. Chapter 8: Building a Data Science Environment Using AWS ML Services

12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services

13. Chapter 10: Advanced ML Engineering

14. Chapter 11: ML Governance, Bias, Explainability, and Privacy

15. Chapter 12: Building ML Solutions with AWS AI Services

16. Other Books You May Enjoy

Kubernetes overview and core concepts

While it is feasible to deploy and manage the life cycle of a small number of containers and containerized applications directly in a compute environment, it can get very challenging when you have a large number of containers to manage and orchestrate across a large number of servers. This is where Kubernetes comes in. Initially released in 2014, Kubernetes (K8s) is an open source system for managing containers at scale on clusters of servers (the abbreviation K8s is derived by replacing ubernete with the digit 8).

Architecturally, Kubernetes operates a master node and one or more worker nodes in a cluster of servers. The master node, also known as the control plane, is responsible  for the overall management of the cluster, and it has four key components:

API server
Scheduler
Controller
etcd

The master node exposes an API server layer that allows programmatic control of the cluster. An example of an API...

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You're reading from The Machine Learning Solutions Architect Handbook Create machine learning platforms to run solutions in an enterprise setting

Table of Contents (17) Chapters

Kubernetes overview and core concepts

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You're reading from The Machine Learning Solutions Architect Handbook Create machine learning platforms to run solutions in an enterprise setting

Table of Contents (17) Chapters

Kubernetes overview and core concepts

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